Possible Applications of Edge Computing in the Manufacturing Industry—Systematic Literature Review

This article presents the results of research with the main goal of identifying possible applications of edge computing (EC) in industry. This study used the methodology of systematic literature review and text mining analysis. The main findings showed that the primary goal of EC is to reduce the time required to transfer large amounts of data. With the ability to analyze data at the edge, it is possible to obtain immediate feedback and use it in the decision-making process. However, the implementation of EC requires investments not only in infrastructure, but also in the development of employee knowledge related to modern computing methods based on artificial intelligence. As the results of the analyses showed, great importance is also attached to energy consumption, both in ongoing production processes and for the purposes of data transmission and analysis. This paper also highlights problems related to quality management. Based on the analyses, we indicate further research directions for the application of edge computing and associated technologies that are required in the area of intelligent resource scheduling (for flexible production systems and autonomous systems), anomaly detection and resulting decision making, data analysis and transfer, knowledge management (for smart designing), and simulations (for autonomous systems).


Introduction
Introducing new and improved tools, machines, devices, or techniques into production processes that increase work efficiency or save raw materials or energy leads to technical progress. In the history of industry so far, we can distinguish four breakthrough concepts that have had a huge impact on production systems: Industry 1.0-water and steam mechanization; Industry 2.0-mass production based on electricity; Industry 3.0-increasing production automation based on digitization; and Industry 4.0-digitalization of manufacturing systems.
Today's industry is shifting towards a green and digital transformation. There is much emphasis on the implementation of sustainable development. Three aspects of sustainable development have been discussed in the literature: economic, environmental, and social. Industry 4.0 technology can support all of these [1]. Sustainable development is associated with all technologies applied to develop the economy with simultaneous care for the preservation of the natural environment and respect for people [2]. Consequently, there is a constant search for approaches and technologies that can support these activities. For example, the concept of zero defect manufacturing, widely explored in [3], can support all aspects of sustainability. This is because, by preventing defects, we prevent the waste of materials and energy, and, thus, excessive costs and additional work that would have to be completed to eliminate defects or to produce a new product without defects. Therefore, the goal is to prevent defects and achieve correct production the first time. Some technologies are based on the analysis of previously collected data to predict problems, such as 1.
A systematic literature review in the field of EC; 2.
Text mining analysis of identified keywords; 3.
Qualitative analysis of abstracts and full texts in terms of technologies used in production areas. The research was conducted to answer the following research questions: RQ1: What topics are discussed in the literature in relation to edge computing (EC)? RQ2: What are the possibilities of using EC in the manufacturing industry?

Systematic Literature Review
In this paper, a systematic literature review was applied. A systematic review allows for a critical and reproducible summary of the results on a specific topic [21].
For the research, three databases were searched: Web of Science, IEEE Xplore, and Scopus. The following inclusion criteria were used in the process: (1) language (English), (2) search in (title, abstract, and keywords), and (3) access (works available in full version).
The following combinations of keywords were used in the searching process: "edge computing" AND "manufacturing", "edge computing" AND "quality control", "edge computing" AND "machining", and "edge computing" AND "production".
The number of publications that appeared in the first search in each database after using a specific combination of keywords is presented in Table 1.
In order to limit the number of publications to those most related to the analyzed issue, the following categories were excluded: agriculture multidisciplinary, biotechnology applied microbiology (Web of Science), agricultural and biological sciences, earth and planetary sciences, environmental science, medicine, multidisciplinary, and social sciences (Scopus).
The data extraction plan is presented in Figure 1.  After applying the exclusion criteria, the following results were obtained: 326 publications in Web of Science, 680 publications in IEEE Explore, and 585 publications in Scopus. Then, a joint database was prepared from which duplicates were removed. Thus, 903 publications remained. Then, the publications without keywords were eliminated. Thus, 722 articles were left.  "edge computing" AND "production" 162 328 281 771 "edge computing" AND "quality control" 6 8 90 104 "edge computing" AND "machining" 13 85 16 114 After applying the exclusion criteria, the following results were obtained: 326 publications in Web of Science, 680 publications in IEEE Explore, and 585 publications in Scopus. Then, a joint database was prepared from which duplicates were removed. Thus, 903 publications remained. Then, the publications without keywords were eliminated. Thus, 722 articles were left.

The Text Mining Procedure
Text mining analysis was performed using VOSviewer [22]. A map was created based on the bibliographic data, i.e., keywords, from 722 publications. It required the preparation of one file with data from all the searched databases. Scopus file structure was applied. For the type of analysis and the counting method, author keyword co-occurrence and full counting were selected. In order to identify the most frequently discussed topics, it was assumed that only author keywords that occurred at least 10 times would be taken into consideration. A total of 2039 different author keywords were identified, and 34 of these met the threshold. Table 2 presents a list of the identified author keywords with information about their occurrences and total link strength. Table 2. An initial list of terms with a threshold of 10 occurrences.

Keyword
Occurrences Total Link  Strength  Keyword  Occurrences  Total Link  Strength   edge computing  346  397  resource allocation  18  33  fog computing  97  186  big data  14  31  cloud computing  76  166  computation offloading  18  29  Internet of Things  75  146  cyber-physical systems  15  28  Industry 4.0  60  129  Industrial IoT  10  27  IoT  47  91  security  14  27  blockchain  36  72  Industrial Internet of Things (IIoT)  22  26  smart factory  23  56  resource management  12  26  smart manufacturing  24  52  game theory  12  25  Internet of Things (IoT)  31  50  digital twin  12  21  machine learning  21  45  energy efficiency  11  21  IIoT  16  43  SDN  14  21  mobile edge computing  37  41  latency  10  20  deep learning  23  40  anomaly detection  11  16  Industrial Internet of Things  25  40  deep reinforcement learning  11  15  5G  23  35  mec  16  14  artificial intelligence  20  35 mobile edge computing (mec) 17 10 It was noticed that there are words with identical meanings in the table. Therefore, a list of synonyms was developed, and their initial name was indicated (Table 3). Then, the final list of author keywords most often appearing in the analyzed articles was prepared ( Table 4). The specified terms were further analyzed. In Section 4.2, we give definitions of these terms. Then, in the next section, the network of terms is visualized. The most common terms identified through text mining were further analyzed and are presented in the next section.

Qualitative Analysis of Data
For qualitative analysis to limit the number of papers for deep review, from 722 publications, we chose only the papers registered in the Web of Science database. We can consider this a limitation of our study because, in non-reviewed papers, other applications can be presented. However, we decided that adopting this rule would be clear that, when looking for further possible application, it would be advisable to read articles from other databases.
After analyzing the abstracts of publications, the publications related to the manufacturing industry were selected for further analysis. The goal of the analysis was to identify the applications of EC in that industry.

Topic Definitions
The most common terms identified through text mining are defined as follows. Edge computing (EC) is the so-called marginal calculations where data are generated and immediately processed at the edge of the network. This technology is necessary to cope with the growing number of communicating devices connected to the network. The goal is to avoid high latency and bottlenecks in cloud computing traffic in networks where several devices both access and generate large amounts of data. Edge computing also improves network support for mobility, security, and privacy [23].
Cyber-physical systems (CPS) are intelligent computer systems that are highly connected, and their physical and computational elements work together [24].
Resource management is the efficient and effective development of an organization's resources when they are needed [40].
Game theory is a mathematical theory of socio-economic phenomena that shows interactions between decision-making units. This theory is based on structural procedures of mathematics and addresses problems from various fields of application [41].
Digital twin is a virtual model that is fully compatible and consistent with a physical object. It simulates object behavior and performance in a real-time environment [42]. Digital twins support sustainable development by saving resources, preventing waste, and, thus, optimizing the effort involved [43].
SDN (software-defined networking) is a programmable network building technology that enables central management and control. It consolidates all control into one node-a network controller (no distributed control architecture). Network relay devices no longer participate in network control and only forward data packets [44].
Anomaly detection uses data mining techniques to detect surprising behaviors hidden in data. When applied to cybersecurity, anomaly detection increases the probability of detecting an attempted break-in or attack [45]. Machine failure can be predicted in machine monitoring with anomaly detection.

Network and Its Visualization
Based on the mapping of keywords that appeared at least 10 times, the network visualization shown in Figure 2 [46] was obtained after the use of thesaurus grouping. In the visualization, each circle represents a specific term. The area of the circle indicates the number of publications with the appropriate term. The thickness of the line joining the terms indicates the total strength of the term co-occurrences in different works [47]. Digital twin is a virtual model that is fully compatible and consistent with a physical object. It simulates object behavior and performance in a real-time environment [42]. Digital twins support sustainable development by saving resources, preventing waste, and, thus, optimizing the effort involved [43].
SDN (software-defined networking) is a programmable network building technology that enables central management and control. It consolidates all control into one node-a network controller (no distributed control architecture). Network relay devices no longer participate in network control and only forward data packets [44].
Anomaly detection uses data mining techniques to detect surprising behaviors hidden in data. When applied to cybersecurity, anomaly detection increases the probability of detecting an attempted break-in or attack [45]. Machine failure can be predicted in machine monitoring with anomaly detection.

Network and Its Visualization
Based on the mapping of keywords that appeared at least 10 times, the network visualization shown in Figure 2 [46] was obtained after the use of thesaurus grouping. In the visualization, each circle represents a specific term. The area of the circle indicates the number of publications with the appropriate term. The thickness of the line joining the terms indicates the total strength of the term co-occurrences in different works [47]. Terms that often coexisted with each other are placed close to each other in the visualization. The terms were grouped into five clusters. The blue cluster in the left area of the visualization consists of terms related to the decrease in data transfer time. The red cluster in the top area covers AI-related terms. In the right and central visualization areas, the green cluster consists of manufacturing-system-related terms. In the central part of the Terms that often coexisted with each other are placed close to each other in the visualization. The terms were grouped into five clusters. The blue cluster in the left area of the visualization consists of terms related to the decrease in data transfer time. The red cluster in the top area covers AI-related terms. In the right and central visualization areas, the green cluster consists of manufacturing-system-related terms. In the central part of the yellow cluster are terms related to locations where data are computed. At the bottom of the map, the purple cluster contains terms related to data volume. The conducted mapping gives a general view of issues related to EC.
The numerical values of term weights and link strengths are presented in Tables 5 and 6, respectively. The weights inform about the importance of the terms. Here, it refers to the number of occurrences of a term in publications. The link strength provides information about the degree of association between the term Edge Computing and a term shown in the first column of Table 6. The absolute value is the number of publications in which both terms occurred in the keywords section. Relative values were calculated as a percent of the maximum value from the given category.
In Table 5, the absolute values of the weights do not sum up in a table row. For example, the number of occurrences of the term Edge Computing in all three databases was 346 and 346 = 138 + 180 + 218. The reasons for this phenomenon are as follows: 1.
The same publication could have been indexed in multiple databases; 2.
The number of occurrence of a term in a database was less than 10 (in such cases, the term was not included in the results).   In Table 6, the sum of the absolute values in the column link strength for all databases was 397, which was the total link strength for the term Edge Computing (see Table 4, row 1). However, as in Table 5, the absolute value of link strength for a selected term does not sum up. The reasons are identical to those mentioned earlier.

Identified Challenges and Technologies Related to EC in Production Systems
An analysis of the full articles retrieved from the WoS database allowed for the identification of EC industrial applications and technologies related to the EC. The results of the analysis are summarized in Table 7.
Apart from edge computing, the following technologies were indicated in the analyzed works: 5G, blockchain, AR, Mixed Reality, HoloLens, discrete-event simulation, big data, CPS, data analytics, data mining, cloud computing, fog computing, fog-edge computing, AI, ML, deep learning, reinforcement learning, deep reinforcement learning, inverse reinforcement learning, neural networks, deep neural network, convolutional neural networks, distributed ensemble learning, dynamic knowledge bases, emotion interaction, facial recognition, image mining, mobile cloud computing, mobile edge computing, mobile edge-cloud computing, particle swarm optimization, evolutionary algorithm, programmable computer network (SDN), and programmable gate arrays. Table 7. EC industrial application and related technologies.

Topics Related to Edge Computing
The aim of this study was to discover the possibility of using edge computing in industry based on the existing scientific evidence. The first research question (RQ1) asked about the topics discussed in the literature in relation to edge computing. The answer was that the most-discussed topics were connected with the Internet of Things, cloud computing, Industrial Internet of Things, and fog computing. The topics that were least discussed in connection with EC were energy efficiency, mobile edge computing, latency, and SDN. These conclusions were drawn from the analysis of Table 6 and Figure 3. The aim of this study was to discover the possibility of using edge computing in in-dustry based on the existing scientific evidence. The first research question (RQ1) asked about the topics discussed in the literature in relation to edge computing. The answer was that the most-discussed topics were connected with the Internet of Things, cloud computing, Industrial Internet of Things, and fog computing. The topics that were least discussed in connection with EC were energy efficiency, mobile edge computing, latency, and SDN. These conclusions were drawn from the analysis of Table 6 and Figure 3.  Table 6, we evaluated content differences between individual bibliographic databases. Table 8   Based on relative link strength (RLS) values from Table 6, we evaluated content differences between individual bibliographic databases. Table 8 presents a proposal of such an evaluation calculated as an arithmetic difference (the RLS of a term for a selected database minus the RLS of a term for the whole set) (Equation (1)): where RLS SINGLE_DB is the relative link strength for a single database and RLS ALL is the relative link strength for all databases. Values of ∆ provide information about the extent to which the content of publications in a single database differs from the whole set. Negative values of ∆ indicate that there are fewer related terms in the given database compared to the whole. In order to obtain an aggregate coefficient of database differences, we proposed calculation of the root mean square (RMS) for the ∆. The calculated values of RMS ∆ were: RMS ∆WOS = 5.3, RMS ∆IEEE_Ex = 4.9, and RMS ∆SCOPUS = 4.3.
Taking into account the obtained numerical values, the following conclusions can be proposed:

1.
In each of the three databases, the most-and the least-discussed topics related to the Edge Computing term were the same as mentioned previously (see Table 6, last four rows); 2.
Compared to the dataset composed of WoS, IEEE Xplore, and Scopus, WoS contained fewer papers in which the term Edge Computing was connected to the terms Cloud Computing, Industry 4.0, Artificial Intelligence, and Resource Allocation; 3.
The IEEE Xplore database had more publications where the term Edge Computing was connected to the term Industrial Internet of Things and fewer connected to the term Digital Twin; 4.
The SCOPUS database contained fewer papers in which the term Edge Computing was connected to the term Industrial Internet of Things; 5.
The overall content of the considered databases in the context of terms related to EC was similar. Therefore, for a more-detailed publication analysis, one database can be chosen instead of the whole set. In such a case, we expect that analysis results will have an error within the limits of RMS ∆ .

Edge Computing Possible Applications
The second research question (RQ2) inquired about the possibilities of using edge computing in the manufacturing industry. These possibilities were discovered by analyzing problems discussed in the reviewed publications. After reviewing 119 papers, 32 main challenges were identified, as presented in Table 7 (Section 3.3). Based on the information included in Table 7, we proposed to distinguish the following 12 main groups based on the area of occurrence.
Group 1: intelligent manufacturing organization in a form of CPS that includes process and data transfer automation, as well as data analytics realized in the cloud and on the edge. The aim is to improve the efficiency of production processes by remote control and optimization of response time. Moreover, distributed AI supports edge networks, which may extend to a supply chain.
Group 2: data management and data security covering data acquisition and transfer, as well as cyber attack prevention. The goal is to collect data and transfer it securely to the destination, while ensuring minimum energy consumption. This requires, among others, implementing appropriate security protocols, identifying threats, and preventing cyber attacks.
Group 3: real-time data processing to monitor the manufacturing process, machines, the quality of the manufactured products, or product performance. In the reviewed papers, different examples were presented, such as monitoring of the work of aircraft engines and their components or production machines and different characteristics. The aim is to evaluate the performance, evaluate the condition, or evaluate the life remaining.
Group 4: quality control supported by technologies. The goal is to shorten and automate quality control processes using historical data, as in the case of virtual metrology systems [3]. Visual inspection and image recognition are also of great importance in this group as detection of anomalies in production can be realized in real time.
Group 5: the use of simulation to improve manufacturing processes. Examples presented in the literature concerned, among others, digital twins that can be used in simulations or in real production monitoring and adjustment.
Group 6: improvement of communication and interactions between machines, as well as between machines and humans. This group is mainly about cooperation remaining undisturbed by communication breaks or erroneous messages that can cause errors, cause delays, and generate costs.
Group 7: facilitating IT system development. The main goal is the development of IT solutions based on data and tailored to needs as much as possible.
Group 8: knowledge management. The goal is to provide relevant knowledge wherever it is needed. An additional advantage is the automatic generation of knowledge based on data.
Group 9: supporting the designing process. In the presented examples, AI was applied to support designing processes in the field of decision making.
Group 10: energy consumption and saving in manufacturing and data management processes. The presented challenges were related to energy savings not only in production, but also in data processing. The necessity to collect large amounts of data increases the energy consumption of devices for recording, analyzing, and transferring data.
Group 11: manufacturing process scheduling and resource allocation. The goal is to involve AI in optimizing the use of resources and planning their involvement in the implementation of production processes with ongoing monitoring of demand and immediate response to it. This is connected with lean manufacturing, Just-in-Time, and pull system concepts, which allow the minimization of resource consumption.
Group 12: robots and autonomous systems. In autonomous and robotic systems, many decisions are made depending on the situation. An unmanned system must react appropriately to the situation in order to achieve the set goal.
Based on the research results, it can be said that EC can be applied in different domains. EC is primarily used because of its ability to collect big data and the increased difficulties with fast data transfer. Although 5G technology is more and more widely used, by implementing EC, it was discovered that it was no longer necessary to transfer all data to the central system, since it can be used on the edge. This can, for example, reduce energy consumption, as well as the space needed for data collection. At the edge, the data can be used in real time, and only those that are unique in some way can be collected and transferred to a central system.

Identified Directions for Further Development and Research in the Areas of Edge Computing Application
The identified challenges (which are described in the previous section) in which EC was applied are summarized in Figure 4. The identified applications of EC are related to CPS. The processes realized in CPS must be scheduled. Therefore, an intelligent resource scheduling strategy can be applied to fulfill the real-time requirement needing to be taken into consideration in smart manufacturing supported by edge computing [145]. The required adjustments to the production schedule may result from changes in customer demand, as well as the quality of the production. This means, for example, that the production of a non-conforming product will generate an automatic schedule change to ensure that a certain number of good products are produced for customers. This is an important direction of development because the introduction of such solutions will eliminate overproduction resulting from the production of more products caused by production planning based on statistics from historical data on the percentage of non-conforming products produced. This is related to the detection of production errors in real time, the identification and classification of defects, and a decision support system in product quality control. Therefore, the development of methods and tools facilitating product control during the production process is important to detect non-conformities [106] at the location of occurrence and not to transfer non-conforming products to the next stage of the production process, which only generates costs and other problems. Not only should the manufactured products be controlled in real time, but most of the implemented processes and devices must be monitored to estimate the possibility of problems [77]. An important direction of further development in this area is the implementation of decision-making systems at the edge to prevent the production of a nonconforming product. Certain ranges of process parameter values (e.g., speed and rotation) or the occurrence of specific values of the operating parameters for machines and devices (e.g., power consumption and temperature) may result in the appearance of a non-compliant product. Therefore, the ability to identify such situations in order to react quickly to them is something desirable to implement in this industry.
The data coming from the monitoring process can be transferred through communication systems to a database to be stored. A "hold-until-changed" approach can be applied to decide which data should be stored. This approach is based on keeping track of earlier transmitted data to determine which data should be transmitted when and to whom [130]. This can minimize data storage requirements and associated costs and result in lower bandwidth utilization, as well as high-speed transmission. It is an important research direction related to sustainable development. In addition, it can reduce the amount of work involved in further data analysis, the results of which will not add any value to the decision-making process.
One of the topics that appears in the literature in an EC context is energy consumption. This can be related to data transfer in CPS [101] and production scheduling considering energy consumption [150]. The number of connected devices in the CPS has increased significantly, which has generated an energy demand. Therefore, we need research and innovative solutions that will not require much energy.
The data coming from the monitoring process can be transferred through communication systems to a database where they can be transformed into knowledge available for employees. Knowledge management is important from many perspectives, for example, in the management of product lifecycle data and in supporting related decision-making processes [66]. Therefore, products can be better-suited to changing requirements. Agile Not only should the manufactured products be controlled in real time, but most of the implemented processes and devices must be monitored to estimate the possibility of problems [77]. An important direction of further development in this area is the implementation of decision-making systems at the edge to prevent the production of a non-conforming product. Certain ranges of process parameter values (e.g., speed and rotation) or the occurrence of specific values of the operating parameters for machines and devices (e.g., power consumption and temperature) may result in the appearance of a non-compliant product. Therefore, the ability to identify such situations in order to react quickly to them is something desirable to implement in this industry.
The data coming from the monitoring process can be transferred through communication systems to a database to be stored. A "hold-until-changed" approach can be applied to decide which data should be stored. This approach is based on keeping track of earlier transmitted data to determine which data should be transmitted when and to whom [130]. This can minimize data storage requirements and associated costs and result in lower bandwidth utilization, as well as high-speed transmission. It is an important research direction related to sustainable development. In addition, it can reduce the amount of work involved in further data analysis, the results of which will not add any value to the decision-making process.
One of the topics that appears in the literature in an EC context is energy consumption. This can be related to data transfer in CPS [101] and production scheduling considering energy consumption [150]. The number of connected devices in the CPS has increased significantly, which has generated an energy demand. Therefore, we need research and innovative solutions that will not require much energy.
The data coming from the monitoring process can be transferred through communication systems to a database where they can be transformed into knowledge available for employees. Knowledge management is important from many perspectives, for example, in the management of product lifecycle data and in supporting related decision-making processes [66]. Therefore, products can be better-suited to changing requirements. Agile adaptation to market needs and flexible production systems are the directions in which enterprises should develop. Modern technologies should support enterprises looking in this direction.
Another identified important direction of research and subsequent industrial implementations is simulations. Although many simulation methods and tools are currently known, their use remains time-consuming and, thus, expensive. The newly designed processes and product operations can be simulated before implementation, as well as after implementation, for example, with the use of digital twins [43]. This is especially important for flexible production systems, as well as for autonomous systems [157]. In this context, the direction of further research that we have identified is the development of methods and tools facilitating the creation of models of the functioning of real objects and simulating their work in various conditions in order to identify potential problems that may arise during operation.

Conclusions
With the use of a systematic review of the literature, it was possible to analyze widely the use of EC. Applied text mining analysis enabled automated text searching of a vast number of publications in order to obtain a new type of information, constituting a quantitative data analysis. A qualitative analysis of the abstracts and full papers allowed the identification of documented application areas of edge computing in industry. The results of the analyses indicated both the technologies used to analyze the identified problems and the documented sources of data.
The presented results are useful material for enterprises looking for answers to questions focusing on smart manufacturing implementation and decision makers in manufacturing companies to determine the possibility of implementing edge computing and related technologies.
The main conclusion of this study was that, despite the large number of publications on edge computing, this field of knowledge is still being developed. It is a promising field of scientific exploration for the future, and many research problems still need to be solved.
Future research in EC should particularly cover areas where actions require immediate decision making and the processing of big data, such as autonomous control of robots, vehicles, or entire factories. The second area of research should be technologies related to mobile edge computing, for example, telecommunications and all issues related to 5G technology.
In particular, on the basis of the conducted research, we have indicated areas that require further research due to the need to implement innovative solutions in the industry. The further research on the application of edge computing and associated technologies is required in the area of intelligent resource scheduling (especially for flexible production systems and autonomous systems), anomaly detection and the resulting decision making (e.g., improper operation of machines, improper implementation of processes, and appearance of non-conforming products), data analysis and transfer (When? How? What? To whom?), knowledge management (knowledge used for smart designing), and simulations (to prevent future problems, especially in autonomous systems).
Further literature research may help identify other directions for the development of EC applications. But the development of effective and practically applicable solutions in the areas indicated by this paper that enterprises will be able to implement would be a significant step towards the development of smart factories.
The systematic literature review also applied to the taxonomy building process. Examples are papers [167] or [168], where the authors presented proposals for the taxonomy of EC in intelligent manufacturing and a general taxonomy for the EC paradigm. The first of the taxonomies contained a section with potential applications of EC in the industry; however, it was limited to a few examples. The second paper did not mention applications of EC in manufacturing. The results of the work presented in this paper can be used to enrich available taxonomies or to create new taxonomies in the area of EC application in the manufacturing industry.